# title
Unsupervised Clustering Under Temporal Feature Volatility in Network Stack Fingerprinting

# abstract
1993-2012 IEEE. Maintaining and updating signature databases are tedious tasks that normally require a large amount of user effort. The problem becomes harder when features can be distorted by observation noise, which we call volatility. To address this issue, we propose algorithms and models to automatically generate signatures in the presence of noise, with a focus on single-probe stack fingerprinting, which is a research area that aims to discover the operating system of remote hosts using their response to a TCP SYN packet. Armed with this framework, we construct a database with 420 network stacks, label the signatures, develop a robust classifier for this database, and fingerprint 66M visible webservers on the Internet. We compare the obtained results against Nmap and discover interesting limitations of its classification process that prevent correct operation when its auxiliary probes (e.g., TCP rainbow, TCP ACK, and UDP to a closed port) are blocked by firewalls.

# publisher.label
Institute of Electrical and Electronics Engineers (IEEE)